Mitigating Performance Degradation in Multi-Source Zero-Shot Cross-Lingual Transfer via Adversarial Training on XTREME

Pre-trained multilingual language encoders, such as multilingual BERT and XLM-R, show great potential for zero-shot cross-lingual transfer. However, these multilingual encoders do not precisely align words and phrases across languages. Especially, learning alignments in the multilingual embedding space usually requires sentence-level or word-level parallel corpora, which are expensive to be obtained for low-resource languages. An alternative is to make the multilingual encoders more robust; when fine-tuning the encoder using downstream task, we train the encoder to tolerate noise in the contex

Research goal: To what extent does adversarial training in multi-source zero-shot cross-lingual transfer mitigate performance degradation across diverse language families in the XTREME benchmark?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.5/10.